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A Summary of Inclusive Pedagogies for Science Education (Mensah and Larson 2017)

Abstract: In this paper, we offer a brief review of six pedagogical and theoretical approaches used in education and science education that we grouped as inclusive pedagogies. Though not an exhaustive list, these pedagogies are more commonly used in educational research and have commonalities, yet are distinctive in some ways. They collectively contribute to making science teaching and learning more inclusive to a broader population of learners, such as students from diverse cultural, linguistic, and social backgrounds and students with physical and learning differences who have traditionally been marginalized in learning science. Furthermore, these inclusive pedagogies aim to decrease educational inequities and raise the level of academic rigor and access for all students. Finally, we discuss ways these inclusive pedagogies can be extended to address reform efforts in science education.

 

http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_189501.pdf

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Kelly Sturner onto Publications

Four-Dimensional Ecology Education Framework 4DEE

The goal of this effort is to produce an ESA-sanctioned framework that can be useful to ecology educators, the ESA Board of Professional Certification process, environmental professionals, decision makers, and others.

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Alycia Crall onto Educational Resources

Test

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Kristin Jenkins onto Comics

R for Data Science: Chapter 4: Communicate

So far, you’ve learned the tools to get your data into R, tidy it into a form convenient for analysis, and then understand your data through transformation, visualisation and modelling. However, it doesn’t matter how great your analysis is unless you can explain it to others: you need to communicate your results.

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Drew LaMar onto Data Life Cycle: Communicate

R for Data Science: Chapter 4: Model

Now that you are equipped with powerful programming tools we can finally return to modelling. You’ll use your new tools of data wrangling and programming, to fit many models and understand how they work. The focus of this book is on exploration, not confirmation or formal inference. But you’ll learn a few basic tools that help you understand the variation within your models.

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Drew LaMar onto Data Life Cycle: Model

R for Data Science: Chapter 3: Program

In this part of the book, you’ll improve your programming skills. Programming is a cross-cutting skill needed for all data science work: you must use a computer to do data science; you cannot do it in your head, or with pencil and paper.

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Drew LaMar onto Data Life Cycle: Program

R for Data Science: Chapter 2: Wrangle

In this part of the book, you’ll learn about data wrangling, the art of getting your data into R in a useful form for visualisation and modelling. Data wrangling is very important: without it you can’t work with your own data!

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Drew LaMar onto Data Life Cycle: Wrangle

R for Data Science: Chapter 1: Explore

The goal of the first part of this book is to get you up to speed with the basic tools of data exploration as quickly as possible. Data exploration is the art of looking at your data, rapidly generating hypotheses, quickly testing them, then repeating again and again and again. The goal of data exploration is to generate many promising leads that you can later explore in more depth.

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Drew LaMar onto Data Life Cycle: Explore

DataCamp Course: Data Visualization in R with lattice

Course Description

Visualization is an essential component of interactive data analysis in R. Traditional (base) graphics is powerful, but limited in its ability to deal with multivariate data. Trellis graphics is the natural successor to traditional graphics, extending its simple philosophy to gracefully handle common multivariable data visualization tasks. This course introduces the lattice package, which implements Trellis graphics for R, and illustrates its basic use.

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Drew LaMar onto Data Life Cycle: Explore

DataCamp Course: Data Visualization in R

Course Description

This course provides a comprehensive introduction on how to plot data with R’s default graphics system, base graphics.

After an introduction to base graphics, we look at a number of R plotting examples, from simple graphs such as scatterplots to plotting correlation matrices. The course finishes with exercises in plot customization. This includes using R plot colors effectively and creating and saving complex plots in R.

Base Graphics Background
R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset.

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Drew LaMar onto Data Life Cycle: Explore

DataCamp Course: Visualization Best Practices in R

Course Description

This course will help you take your data visualization skills beyond the basics and hone them into a powerful member of your data science toolkit. Over the lessons we will use two interesting open datasets to cover different types of data (proportions, point-data, single distributions, and multiple distributions) and discuss the pros and cons of the most common visualizations. In addition, we will cover some less common alternatives visualizations for the data types and how to tweak default ggplot settings to most efficiently and effectively get your message across.

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Drew LaMar onto Data Life Cycle: Explore

DataCamp Course: Data Visualization with ggplot2 (Part 2)

Course Description

This ggplot2 tutorial builds on your knowledge from the first course to produce meaningful explanatory plots. We'll explore the last four optional layers. Statistics will be calculated on the fly and we’ll see how Coordinates and Facets aid in communication. Publication quality plots will be produced directly in R using the Themes layer. We’ll also discuss details on data visualization best practices with ggplot2 to help make sure you have a sound understanding of what works and why. By the end of the course, you’ll have all the tools needed to make a custom plotting function to explore a large data set, combining statistics and excellent visuals.

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Drew LaMar onto Data Life Cycle: Explore

DataCamp Track: Data Visualization with R

Communicate the most important features of your data by creating beautiful visualizations using ggplot2 and base R graphics.

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Drew LaMar onto Data Life Cycle: Explore

DataCamp Course: Data Visualization with ggplot2 (Part 1)

Course Description

The ability to produce meaningful and beautiful data visualizations is an essential part of your skill set as a data scientist. This course, the first R data visualization tutorial in the series, introduces you to the principles of good visualizations and the grammar of graphics plotting concepts implemented in the ggplot2 package. ggplot2 has become the go-to tool for flexible and professional plots in R. Here, we’ll examine the first three essential layers for making a plot - Data, Aesthetics and Geometries. By the end of the course you will be able to make complex exploratory plots.

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Drew LaMar onto Data Life Cycle: Explore

DataCamp Course: Building Dashboards with shinydashboard

Course Description

Once you've started learning tools for building interactive web applications with shiny, this course will translate this knowledge into building dashboards. Dashboards, a common data science deliverable, are pages that collate information, often tracking metrics from a live-updating data source. You'll gain more expertise using shiny while learning to build and design these dynamic dashboards. In the process, you'll pick up tips to optimize performance as well as best practices to create a visually appealing product.

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Drew LaMar onto Data Life Cycle: Communicate

DataCamp Course: Building Web Applications in R with Shiny: Case Studies

Course Description

After learning the basics of using Shiny to build web applications, this course will take you to the next level by putting your newly acquired skills into use. You'll get experience developing fun and realistic Shiny apps for different common use cases, such as using Shiny to explore a dataset, to generate a customized plot, and even to create a wordcloud. With all this practice and new knowledge, you should be inspired and well-equipped to develop Shiny apps for your own use!

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Drew LaMar onto Data Life Cycle: Communicate

DataCamp Course: Building Web Applications in R with Shiny

Course Description

Shiny is an R package that makes it easy to build interactive web apps straight from R. Shiny combines the computational power of R with the interactivity of the modern web. This course will take you from R programmer to Shiny developer. If you want to take a fresh, interactive approach to telling your data story, let users interact with your data and your analysis, and do it all with R, dive in!

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Drew LaMar onto Data Life Cycle: Communicate

DataCamp Course: Building Dashboards with flexdashboard

Course Description

Communication is a key part of the data science process. Dashboards are a popular way to present data in a cohesive visual display. In this course you'll learn how to assemble your results into a polished dashboard using the flexdashboard package. This can be as simple as adding a few lines of R Markdown to your existing code, or as rich as a fully interactive Shiny-powered experience. You will learn about the spectrum of dashboard creation tools available in R and complete this course with the ability to produce a professional quality dashboard.

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Drew LaMar onto Data Life Cycle: Communicate

DataCamp Course: Reporting with R Markdown

Course Description

Learn how to write a data report quickly and effectively with the R Markdown package, and share your results with your friends, colleagues or the rest of the world. Learn how you can author your own R Markdown reports, and how to automate the reporting process so that you have your own reproducible reports. By the end of the interactive data analysis reporting tutorial, you will be able to generate reports straight from your R code, documenting your work — and its results — as an HTML, pdf, slideshow or Microsoft Word document.

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Drew LaMar onto Data Life Cycle: Communicate

DataCamp Course: Introduction to Git for Data Science

Course Description

Version control is one of the power tools of programming. It allows you to keep track of what you did when, undo any changes you have decided you don't want, and collaborate at scale with other people. This course will introduce you to Git, a modern version control tool that is very popular with data scientists and software developers alike, and show you how it can help you get more done in less time and with less pain.

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Drew LaMar onto Data Life Cycle: Program

DataCamp Course: Working with the RStudio IDE (Part 2)

Course Description

Learn how RStudio makes it easy to build your own R packages, how you can integrate with your favorite version control software like Git and GitHub for maximum productivity, and finally how to make use of tools like R Markdown, LaTeX, and Shiny for reporting your results to the relevant stakeholders.

This is the second part of a two-part course on how to use RStudio. Part 1 covers the basics of working with RStudio.

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Drew LaMar onto Data Life Cycle: Program

DataCamp Course: Working with the RStudio IDE (Part 1)

Course Description

In the first part of this two-part RStudio tutorial, you will learn how to use RStudio, an IDE for R.

Chapter 1 will cover:

  1. how to install R Studio for Mac, Windows, or Linux
  2. how to run R code
  3. debugging in R

Chapter 2 will get into the features of the IDE geared toward making you a more effective R programmer, including code diagnostics and running scripts.

Finally, Part 1 finishes with a brief chapter on using RStudio projects for organizing and sharing your code with others.

In Part 2, you will learn how RStudio makes it easy to build your own R packages, integrate with GitHub, and use tools like R Markdown.

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Drew LaMar onto Data Life Cycle: Program

DataCamp Course: Introduction to the Tidyverse

Course Description

This is an introduction to the programming language R, focused on a powerful set of tools known as the "tidyverse". In the course you'll learn the intertwined processes of data manipulation and visualization through the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. This gives a taste both of the value of exploratory data analysis and the power of tidyverse tools. This is a suitable introduction for people who have no previous experience in R and are interested in learning to perform data analysis.

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Drew LaMar onto Data Life Cycle: Program

DataCamp Course: Writing Functions in R

Course Description

Functions are a fundamental building block of the R language. You've probably used dozens (or even hundreds) of functions written by others, but in order to take your R game to the next level, you'll need to learn to write your own functions. This course will teach you the fundamentals of writing functions in R so that, among other things, you can make your code more readable, avoid coding errors, and automate repetitive tasks.

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Drew LaMar onto Data Life Cycle: Program

DataCamp Course: Intermediate R - Practice

Course Description

This follow-up course on Intermediate R does not cover new programming concepts. Instead, you will strengthen your knowledge of the topics in Intermediate R with a bunch of new and fun exercises.

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Drew LaMar onto Data Life Cycle: Program